Sam Gutentag
AI-powered development has become the new baseline. At Google, 25% of new code is now AI-generated. Meta is pushing AI pair programmers across teams. The ecosystem of AI dev tools is growing faster than most teams can evaluate them.
And yet, when it comes to debugging, even the best models still struggle. Microsoft’s latest research confirms it. The top models from Anthropic topped out around 48% success rates and OpenAI’s best marks are just over 30% when solving real-world bugs from SWE-bench Lite, even when given access to debuggers and support tools.
This is not a sign of failure; rather, AI as we know it today might just be evolving into a developer force multiplier, not a replacement.
Debugging Is Hard for a Reason
Debugging has proven to be one of the most human parts of software engineering. It requires exploration, hypothesis testing, and an intuitive grasp of how systems behave under stress. It is not just about spotting syntax errors or copy-pasting loops in your LLM supercharged IDE of choice. It’s about understanding how and why something breaks.
Microsoft’s study highlights that AI models often fail not because they are incapable, but because they have not yet been trained on enough real-world debugging journeys. These journeys include not just final bug fixes but the steps in between: reading logs, trying experiments, checking assumptions.
Once models get access to that type of trajectory data, expect rapid gains. Until then, the takeaway is simple: AI is not the debugger, but it can still be the best assistant in the room.
AI Tools Keep Dev Velocity Alive
This is where the story turns. Because even if models can’t solve every edge case bug, they are already keeping teams unblocked in meaningful ways.
Post-MVP, most developer time gets eaten by tasks that are necessary but not particularly fulfilling. Pre-commit checks. PR triage. Formatting wars. Chasing CI flakes. AI DevOps Agents like the one from Trunk are purpose-built to handle exactly that kind of work.
Instead of managing a bloated pipeline of tools and config files, teams can use a single AI agent that coordinates linting, testing, and merge logistics. This is the real AI acceleration story. Not writing perfect code in one shot, but removing friction so developers can keep momentum.
Velocity is what gets features out the door. AI tools are how teams keep it.
From Code Generator to Flow Enabler
The goal of AI in software development isn’t to eliminate the engineer. It’s to protect engineering time. By taking over the repetitive, time-sensitive, and brittle parts of the workflow, AI allows teams to focus on system design, architecture, and innovation.
When AI can’t fix a bug, it can still surface relevant logs, it can still write a failing test, it can still prep a PR or summarize the context. These small wins accumulate into big time savings.
This is the story behind tools like the Trunk AI DevOps Agent. Instead of aiming to do everything, it aims to do the right things that help developers stay unblocked and productive. It works quietly in the background, keeping the flow state alive and the deployment train running.
AI doesn’t need to be perfect to be powerful. Let it remove the friction so teams can keep shipping. Explore how the Trunk AI DevOps Agent helps scale development by handling the toil and keeping teams in flow.
Join the waitlist to experience Trunk’s AI DevOps Agent and put your CI pipeline on autopilot. https://trunk.io/agent
